Delayed treatment for acute coronary syndrome (ACS) can be a matter of life or death. Each 30-minute increase in treatment delay raises the one-year mortality rate by 7.5%. The median patient prehospital delay time in presentation to the emergency department (ED) is more than two hours and has ranged from 1-24 hours, raising the risk of one year mortality up to an astounding 360% across studies. Emergent reperfusion targets (<90 minutes) for ST-elevation myocardial infarction (STEMI) have been largely met with no resultant decrease in mortality suggesting that prehospital, rather than in-hospital delay, is contributing to poorer patient outcomes. In addition, ACS is associated with multiple debilitating symptoms, impaired functional status, repeat visits to the ED, and frequent healthcare utilization but inadequate attention has been paid to the relationship between prehospital delay (defined as time of symptom onset to arrival in the ED) and these patient outcomes. The objective of this secondary data analysis is to identify multiple, theory-based factors associated with prehospital delay and examine the association of prehospital delay with the outcomes of healthcare utilization and persistent symptoms. The sample includes patients presenting to the ED with symptoms that triggered an evaluation for possible ACS. Data from the prospective Think Symptoms study, whose main aim was to characterize the influence of gender on symptoms during ACS, will be used for analyses. The dataset includes a multiethnic sample of 1064 patients (400 women and 664 men) presenting to the ED with symptoms that triggered a cardiac evaluation. Data were collected in four academic and one non- academic medical center in four regions of the US. The dataset is unique in that it includes patients ruled-in for ACS and ruled-out for ACS as well as symptom data collected at presentation to triage, in the ED examination room, and one and six months following discharge. Specific aims of this study are to: 1) Identify predictors of prehospital delay for potential ACS by final diagnosis (ACS vs. no ACS) including patient factors, event factors, and transportation factors; 2) Determine the association between prehospital delay and one and six-month health care utilization and symptom outcomes by diagnosis (ACS vs. no ACS); and 3) Determine the relative contribution of patient, event, and transportation factors on patient outcomes attributable to prehospital delay. Multilinear and logistic regression modeling adjusting for relevant covariates will be used to address aims 1 and 2. Path analyses, a form of statistical equation modeling will be used to address aim 3. This novel work will provide models of patient factors influencing delay that will fill knowledge gaps and aid in the design of patient-centered interventions to reduce prehospital delay for ACS and improve patient outcomes.